Risk Modeling Research Articles

Insuring Data Risks: Problems and Solutions

The proliferation of valuable data assets and connectivity in the digital economy has been accompanied by intensifying cyber risks. However, systemic constraints including data ambiguities, legal uncertainty, and misaligned incentives have severely limited advancement of cyber insurance coverage relative to rising enterprise protection needs. This research provides a comprehensive analysis of key bottlenecks inhibiting cyber data risk insurability. It examines constraints stemming from historical data deficiencies, risk modeling complexities, opaque controls, and fragmented regulatory regimes. The study also evaluates internal challenges faced by insurers in advancing policies like claims ambiguities, talent gaps, and reliance on primitive actuarial techniques. It further proposes targeted legal, risk management and public-private partnership enhancements that can expand viable transfer of cyber data risks. These include graduated security frameworks, transparent data exchanges, resilience incentives, risk pooling structures and international cooperation. With balanced reforms, cyber insurance can systematically enable enterprises to secure data assets commensurate with their rising economic and societal value.

Beyond Conventional Threat Defense: Implementing Advanced Threat Modeling Techniques, Risk Modeling Frameworks and Contingency Planning in the Healthcare Sector for Enhanced Data Security

This research study explores the integration of advanced threat and risk modeling with scenario and contingency planning to enhance security and resilience within healthcare organizations. Given the escalating complexity and frequency of cyber threats targeting the healthcare sector, the study assesses the efficacy of a synergistic cybersecurity approach. Utilizing a quantitative research methodology, data were collected through a survey administered to 452 healthcare practitioners, focusing on their perceptions and experiences with cybersecurity threats, risk modeling techniques, and the effectiveness of scenario and contingency planning. The survey incorporated a structured questionnaire utilizing Likert scale closed-ended questions, analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the proposed four hypotheses related to the impact of advanced threat and risk modeling and scenario and contingency planning on the cybersecurity posture and operational resilience of healthcare organizations. Results indicated a significant positive relationship between advanced threat modeling and scenario planning with the cybersecurity posture of healthcare organizations, which, in turn, notably enhances organizational resilience. Specifically, the study found that integrating advanced threat and risk modeling with scenario and contingency planning significantly improves the ability of healthcare organizations to identify, assess, and mitigate cyber threats effectively. Furthermore, the findings suggest that such integration contributes to more informed decision-making, improved information security management practices, and more effective and efficient digital crime investigation processes. The research study concludes that a comprehensive approach integrating advanced threat and risk modeling with scenario and contingency planning significantly enhances healthcare organizations' cybersecurity posture and operational resilience. The study recommends adopting an integrated cybersecurity strategy, continuous cybersecurity education for all organizational members,regular cybersecurity assessments, and fostering collaboration for enhanced cybersecurity intelligence.

Unoccupied aerial vehicles as a tool to map lizard operative temperature in tropical environments

AbstractTo understand how ectotherms will respond to warming temperatures, we require information on thermal habitat quality at spatial resolutions and extents relevant to the organism. Measuring thermal habitat quality is either limited to small spatial extents, such as with ground‐based 3D operative temperature (Te) replicas, representing the temperature of the animal at equilibrium with its environment, or is based on microclimate derived from physical models that use land cover variables and downscale coarse climate data. We draw on aspects of both these approaches and test the ability of unoccupied aerial vehicle (UAV) data (optical RGB) to predict fine‐scale heterogeneity in sub‐canopy lizard (Anolis bicaorum) Te in tropical forest using random forest models. Anolis bicaorum is an endemic, critically endangered, species, facing significant threats of habitat loss and degradation, and work was conducted as part of a larger project. Our findings indicate that a model incorporating solely air temperature, measured at the centre of the 20 × 20 m plot, and ground‐based leaf area index (LAI) measurements, measured at directly above the 3D replica, predicted Te well. However, a model with air temperature and UAV‐derived canopy metrics performed slightly better with the added advantage of enabling the mapping of Te with continuous spatial extent at high spatial resolutions, across the whole of the UAV orthomosaic, allowing us to capture and map Te across the whole of the survey plot, rather than purely at 3D replica locations. Our work provides a feasible workflow to map sub‐canopy lizard Te in tropical environments at spatial scales relevant to the organism, and across continuous areas. This can be applied to other species and can represent species within the same community that have evolved a similar thermal niche. Such methods will be imperative in risk modelling of such species to anthropogenic land cover and climate change.

Risk Modeling, Return Forecasting, and Optimal Portfolio Selection

The main objective of this research is to form an optimal investment portfolio consisting of a number of stocks selected according to specific criteria in Amman Stock Exchange and to test the ability of a various economic models to predict the performance of this portfolio in the foreseeable future. A time series for the return of the selected portfolio and for the return of a market index are formed. A set of tests were conducted to reach a stationary time series return and, then, to follow the Box-Jenkins methodology in order to build predictive models (ARMA) and to examine the residuals of models and to model them using ARCH and GARCH models to reach the best prediction of the performance of the portfolio and the market index in the forecasted periods. The data were tracked on a daily basis for the study sample and the market index simultaneously for a period of three years. Twenty-one companies were selected in the investment portfolio distributed among several sectors. The study concluded that the formed portfolio achieved a good diversification and gave a high return in relation to the lowest possible risk according to the Sharpe scale. Also, it is concluded that the model ARMA (1,1) is the most suitable for estimating market portfolio returns and forecasting risks for the market index return, and ARMA (2,1) and the model ARMA - GARCH are the most capable one of achieving good results that can be relied upon in tracking the performance of the studied investment portfolio.

Unlocking effective ice-jam risk management: Insights from agent-based modeling and comparative analysis of social theories in Fort McMurray, Canada

Ice jams pose a major flood hazard in communities along northern rivers, resulting in high backwater levels and overbank flooding during ice-cover breakup. Mitigation measures, including large-scale projects by government agencies and individual actions by asset owners, can help reduce flood risk and protect human life and assets. This study examines ice-jam flood risk and explores the effectiveness of adaptive strategies in mitigating such risks in Fort McMurry, Canada. It evaluates the effectiveness of top-down (government-led artificial breakup) and bottom-up (resident-led flood-proofing) strategies, comparing the Rational Choice Theory (RCT) and the Protection Motivation Theory (PMT) models. The objective is to explore the potential enhancements to the ice-jam flood risk model through the integration of the PMT as a decision-making framework under uncertainty. This study seeks to assess whether and to what extent such integration can improve the modeling of ice-jam flood risk. The findings highlight the benefits of incorporating socio-economic factors in the PMT model. Economic factors, such as income tax and the cost of flood-proofing, shape overall flood risk, especially when artificial breakup measures are not implemented. The study emphasizes the importance of considering heterogeneity in decision-making processes and diverse characteristics of individuals when designing flood risk management strategies. Response efficacy and self-efficacy coefficients are significant factors influencing flood risk and the adoption of flood-proofing measures. Enhancing individuals' belief in their actions' effectiveness and their confidence in self-protection contributes to more effective flood risk management. These findings inform the development of more effective flood risk management strategies.

Inhomogeneous log-Gaussian Cox processes with piecewise constant covariates: a case study in modeling of COVID-19 transmission risk in East Java

Inhomogeneous log-Gaussian Cox processes with piecewise constant covariates: a case study in modeling of COVID-19 transmission risk in East Java

Specific mortality in patients with diffuse large B-cell lymphoma: a retrospective analysis based on the surveillance, epidemiology, and end results database.

The full potential of competing risk modeling approaches in the context of diffuse large B-cell lymphoma (DLBCL) patients has yet to be fully harnessed. This study aims to address this gap by developing a sophisticated competing risk model specifically designed to predict specific mortality in DLBCL patients. We extracted DLBCL patients' data from the SEER (Surveillance, Epidemiology, and End Results) database. To identify relevant variables, we conducted a two-step screening process using univariate and multivariate Fine and Gray regression analyses. Subsequently, a nomogram was constructed based on the results. The model's consistency index (C-index) was calculated to assess its performance. Additionally, calibration curves and receiver operator characteristic (ROC) curves were generated to validate the model's effectiveness. This study enrolled a total of 24,402 patients. The feature selection analysis identified 13 variables that were statistically significant and therefore included in the model. The model validation results demonstrated that the area under the receiver operating characteristic (ROC) curve (AUC) for predicting 6-month, 1-year, and 3-year DLBCL-specific mortality was 0.748, 0.718, and 0.698, respectively, in the training cohort. In the validation cohort, the AUC values were 0.747, 0.721, and 0.697. The calibration curves indicated good consistency between the training and validation cohorts. The most significant predictor of DLBCL-specific mortality is the age of the patient, followed by the Ann Arbor stage and the administration of chemotherapy. This predictive model has the potential to facilitate the identification of high-risk DLBCL patients by clinicians, ultimately leading to improved prognosis.

Dynamic evaluation of road and bridge engineering construction safety risk based on Fuzzy Dynamic Bayesian Network research

To comprehensively and objectively evaluate the actual safety condition in road and bridge engineering construction, the road and bridge engineering construction safety risk evaluation index system is constructed combined with the factors induced by emergencies in the road and bridge engineering construction process. Aiming at the dynamic uncertainty of road and bridge construction safety risk, using Fuzzy Set Theory and an improved similar aggregation method to determine the prior probabilities and conditional probabilities of network nodes, and then selecting the transition probabilities of nodes through expert opinions and incident reports, leading to the development of a dynamic evaluation model for safety risks in road and bridge engineering construction based on Fuzzy Dynamic Bayesian Network, this model can make the construction safety risk prediction result accurately. Taking the Hebi City Provincial Highway 304 reconstruction project as an example for analysis, the results indicate that the model can accurately predict the probability of changes in safety risks in road and bridge engineering construction. Additionally, it can identify critical risk factors and provide crucial supporting information for decision-makers to optimize risk management strategies.

Environmental risk assessment of underground concentrated brine reservoir with solute transport model: A case study of a coal mine in Northwest China

Underground reservoirs have emerged as a promising technology for storing and reusing saline wastewater from coal mines. However, the high salinity and potential leakage risks pose environmental threats needing assessment. This study developed an integrated solute transport and relative risk modeling framework to evaluate the environmental risks of an underground concentrated brine reservoir (UCBR) in Northwest China. The solute transport model simulated subsurface brine migration under long-term penetration, sudden leakage, and complete leakage scenarios, parameterized by field data. The relative risk model integrated source, receptor, and exposure analyses to quantify normalized risk levels. Results revealed that risks incrementally rose over longer storage durations, with 70 years posing high risk under 10 m hydraulic gradients in the long-term scenario. The risk of sudden leakage is controllable within 30 days but dramatically elevated by 60 days. Complete leakage was predicted to affect a stable 3 km2 zone. The integrated modeling provides an improved methodology for assessing UCBR risks and highlights the critical influence of bottom barrier permeability, groundwater gradients, and storage time. Targeted strategies are proposed, including reinforcing bottom barriers, managing hydraulic gradients, controlling storage durations, implementing early warning systems, and contingency planning to promote sustainable mining water management.

Risk factors for and risk of all-cause and atherosclerotic cardiovascular disease mortality in people with type 2 diabetes and peripheral artery disease: an observational, register-based cohort study

BackgroundType 2 diabetes (T2D) and peripheral artery disease (PAD) are recognized as independent risk factors contributing to excess mortality. Contemporary observational studies exploring the associations of risk factors, and risk of all-cause and atherosclerotic cardiovascular disease mortality in persons with T2D following the onset of incident peripheral artery disease are limited. The objectives of this study were to investigate the associations of risk factors, and assess mortality risks in people with T2D compared with controls without T2D after the onset of PAD.MethodsAll persons with T2D (n = 150,215) registered in the Swedish National Diabetes Register between 2005 and 2009 were included, along with 346,423 controls without T2D matched for sex and age. Data were retrieved from several national registries, capturing information on risk factors, onset of incident peripheral artery disease, other comorbidities, socioeconomic factors, and outcomes. To compare persons with T2D and controls following the onset of peripheral artery disease regarding the risk of all-cause, and atherosclerotic cardiovascular disease mortality, Cox proportional hazard models and Kaplan-Meier curves were employed. A gradient-boosting model was utilized to estimate the relative statistical contribution of risk factors to the modeling of incident mortality risk in people with both T2D and peripheral artery disease.ResultsCrude rates of incident all-cause mortality were higher in individuals with T2D compared with controls, following the onset of PAD (600.4 (95% CI, 581.4-619.8) per 10,000 person-years versus 549.1 (95% CI, 532.1-566.5) per 10,000 person-years). Persons with T2D had an adjusted hazard ratio (HR) for all-cause mortality of 1.12 (95% CI, 1.05–1.19, P < 0.01) compared with controls after onset of incident PAD. The comparable adjusted HR for cardiovascular mortality was 1.13 (95% CI, 1.07–1.19, P < 0.01). High age and hyperglycemia at baseline played a significant role in contributing to the predictive models for incident all-cause and cardiovascular mortality among individuals with both T2D and PAD.ConclusionsThe presence of T2D with concomitant PAD is related to an increased risk of both all-cause and cardiovascular mortality compared with individuals with only PAD. This argues for implementing optimized and intensive treatment strategies for individuals with both conditions.

Predictive modeling of deep vein thrombosis risk in hospitalized patients: A Q-learning enhanced feature selection model

Deep vein thrombosis (DVT) represents a critical health concern due to its potential to lead to pulmonary embolism, a life-threatening complication. Early identification and prediction of DVT are crucial to prevent thromboembolic events and implement timely prophylactic measures in high-risk individuals. This study aims to examine the risk determinants associated with acute lower extremity DVT in hospitalized individuals. Additionally, it introduces an innovative approach by integrating Q-learning augmented colony predation search ant colony optimizer (QL-CPSACO) into the analysis. This algorithm, then combined with support vector machines (SVM), forms a bQL-CPSACO-SVM feature selection model dedicated to crafting a clinical risk prognostication model for DVT. The effectiveness of the proposed algorithm's optimization and the model's accuracy are assessed through experiments utilizing the CEC 2017 benchmark functions and predictive analyses on the DVT dataset. The experimental results reveal that the proposed model achieves an outstanding accuracy of 95.90% in predicting DVT. Key parameters such as D-dimer, normal plasma prothrombin time, prothrombin percentage activity, age, previously documented DVT, leukocyte count, and thrombocyte count demonstrate significant value in the prognostication of DVT. The proposed method provides a basis for risk assessment at the time of patient admission and offers substantial guidance to physicians in making therapeutic decisions.

Prevalence and associated factors of sexual, psychological, and physical violence among physical therapists in their clinical role in Spain: a national web-based cross-sectional survey.

To determine the extent of career-long and 12-month exposure to sexual, physical, and psychological/verbal violence committed by patients or their companions among physical therapists in Spain. Additionally, to identify the factors associated with such exposure. This study employed an observational cross-sectional approach. Initially, a questionnaire was developed and validated using a convenience sample. Subsequently, it was distributed via email to all physical therapists registered in Spain in the first quarter of 2022. Individual risk models were created for each type of violence experienced within the past 12months. The prevalence of violence encountered by physical therapists throughout their careers was 47.9% for sexual violence, 42.7% for psychological/verbal abuse, and 17.6% for physical abuse. Lower values were observed within the last 12months (13.4%, 15.8%, and 5.2%, respectively). Statistical risk modeling for each type of violence experienced in the past 12months indicated that the common precipitating factor for all forms of violence was working with patients with cognitive impairment. Working part-time appeared to be a protective factor. Other factors, such as the practitioners' gender, practice setting, or clinic location showed variations among the diverse types of violence. The exposure to type II workplace violence within the last 12months among physical therapists in Spain (Europe) is not so high as in some other world regions. Various individual, clinical, and professional/organizational risk factors have been identified in connection with type II workplace violence. Further research is warranted to compare the violence experienced once the COVID pandemic has subsided.

Empirical modeling of agricultural climate risk

Effective policies for adaptation to climate change require understanding how impacts are related to exposures and vulnerability, the dimensions of the climate system that will change most and where human impacts will be most draconian, and the institutions best suited to respond. Here, we propose a simple method for more credibly pairing empirical statistical damage estimates derived from recent weather and outcome observations with projected future climate changes and proposed responses. We first analyze agricultural production and loan repayment data from Brazil to understand vulnerability to historical variation in the more predictable components of temperature and rainfall (trend and seasonality) as well as to shocks (both local and over larger spatial scales). This decomposed weather variation over the past two decades explains over 50% of the yield variation in major Brazilian crops and, critically, can be constructed in the same way for future climate projections. Combining our estimates with bias-corrected downscaled climate simulations for Brazil, we find increased variation in yields and revenues (including more bad years and worse outcomes) and higher agricultural loan default at midcentury. Results in this context point to two particularly acute dimensions of vulnerability: Intensified seasonality and local idiosyncratic shocks both contribute to worsening outcomes, along with a reduced capacity for spatially correlated ("covariate") shocks to ameliorate these effects through prices. These findings suggest that resilience strategies should focus on institutions such as water storage, financial services, and reinsurance.

Improving the accuracy of credit scoring models using an innovative Bayesian informative prior specification method

A new Bayesian informative prior specification method (BAF method – Bayesian priors using ARIMA forecasts) is proposed to introduce additional information into credit risk modelling and improve model predictive performance. We use logistic regression to model the probability of default of mortgage loans comparing the Bayesian approach with various priors and the frequentist approach. But unlike previous literature, we treat coefficient estimates in the probability of default model as stochastic time series variables. We build ARIMA models to forecast the coefficient values in future time periods and use these ARIMA forecasts as Bayesian informative priors. We find that the Bayesian models using this prior specification method produce more accurate predictions for the probability of default as compared to frequentist models and Bayesian models with other priors.

Factor Analysis Framework for Credit, Operational, and Market Risk Modeling

Factor Analysis Framework for Credit, Operational, and Market Risk Modeling

Developing and validating machine learning-based prediction models for frailty occurrence in those with chronic obstructive pulmonary disease.

Frailty is a medical syndrome caused by multiple factors, characterized by decreased strength, endurance, and diminished physiological function, resulting in increased susceptibility to dependence and/or death. Patients with chronic obstructive pulmonary disease (COPD) tend to be more vulnerable to frailty due to their physical and psychological burdens. Therefore, the aim of this study was to develop a reliable and accurate vulnerability risk prediction model for frailty in patients with COPD in order to improve the identification and prediction of patient frailty. The specific objectives of this study were to determine the prevalence of frailty in patients with COPD and develop a prediction model and evaluate its predictive power. Clinical information was analyzed using data from the 2018 China Health and Retirement Longitudinal Study (CHARLS) database, and 34 indicators, including behavioral factors, health status, mental health parameters, and various sociodemographic variables, were examined in the study. The adaptive synthetic sampling technique was used for unbalanced data. Three methods, ridge regressor, extreme gradient boosting (XGBoost) classifier, and random forest (RF) regressor, were used to filter predictors. Seven machine learning (ML) techniques including logistic regression (LR), support vector machines (SVM), multilayer perceptron, light gradient-boosting machine, XGBoost, RF, and K-nearest neighbors were used to analyze and determine the optimal model. For customized risk assessment, an online predictive risk modeling website was created, along with Shapley additive explanation (SHAP) interpretations. Depression, smoking, gender, social activities, dyslipidemia, asthma, and residence type (urban vs. rural) were predictors for the development of frailty in patients with COPD. In the test set, the XGBoost model had an area under the curve of 0.942 (95% confidence interval: 0.925-0.959), an accuracy of 0.915, a sensitivity of 0.873, and a specificity of 0.911, indicating that it was the best model. The ML predictive model developed in this study is a useful and easy-to-use instrument for assessing the vulnerability risk of patients with COPD and may aid clinical physicians in screening high-risk patients.

Where and how often do people touch train interiors? An investigation during the pandemic

The coronavirus disease-2019 pandemic highlighted infection and hygiene as risks in dense public spaces, including public transport vehicles. This study investigated passenger touching behaviour within metro vehicles to understand the fomite exposure of passengers. Using in-vehicle CCTV on three lines of the London Underground, this paper investigated the number of touches on different types of train interiors and investigated their relationship with passenger density and movement. In total, 1818 station sections and 16 891 passengers were observed cumulatively from May to December 2021. The results showed that on average for each type of interiors on Victoria and Jubilee lines, there were between 0.07 and 0.57 touches per passenger movement (i.e. boarding or alighting) for deep tube lines, whereas District line that uses larger carriages showed a different tendency. The results also suggested that for deep tube lines, the number of touches per person increased beyond a density of ∼1.5 standing passengers/m2 possibly because passengers may touch interiors more in order not to bump into other passengers. These findings can be used in infection risk modelling and can inform mitigation of infection risk of future pandemics.

The role of technology in supply chain risk management: Innovations and challenges in logistics

The review delves into the pivotal role of technology in revolutionizing supply chain risk management practices within the logistics sector. The paper explores the myriad of risks faced by modern supply chains, ranging from natural disasters and geopolitical tensions to cyber threats and disruptions in global trade patterns. It investigates how technological innovations such as blockchain, Internet of Things (IoT), artificial intelligence (AI), and predictive analytics are reshaping traditional risk management approaches by providing real-time visibility, data-driven insights, and proactive mitigation strategies. Through a comprehensive analysis, the study examines the transformative potential of these technologies in enhancing supply chain resilience, agility, and responsiveness to unforeseen disruptions. It highlights the benefits of leveraging blockchain technology for secure and transparent supply chain transactions, IoT sensors for real-time monitoring of goods in transit, AI algorithms for predictive risk modeling, and predictive analytics for identifying and mitigating potential disruptions before they escalate. Furthermore, the paper delves into the challenges and complexities associated with adopting and integrating these technologies into existing supply chain processes, including interoperability issues, data privacy concerns, and the need for specialized expertise. By providing practical insights, case studies, and best practices, the study aims to empower logistics professionals, policymakers, and industry stakeholders to harness the full potential of technology-driven solutions in managing supply chain risks effectively and ensuring business continuity in an increasingly uncertain and volatile global environment.

Assessment of Climate Change Risk on Insurance Portfolios

Purpose: The general purpose of this study was to investigate the assessment of climate change risk on insurance portfolios.&#x0D; Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library.&#x0D; Findings: The findings reveal that there exists a contextual and methodological gap relating to climate change risk on insurance portfolios. The assessment of climate change risk on insurance portfolios reveals the significant challenges posed by increasingly frequent and severe climate-related events, impacting insurers' financial stability and policyholders' security. Key conclusions emphasize the imperative for insurers to enhance climate risk modeling, integrate climate considerations into decision-making processes, and foster collaboration with stakeholders to develop effective risk management solutions. Proactive measures are essential to address the systemic nature of climate risks, underscoring the urgency of collective action and innovation to build resilience and sustainability in insurance portfolios and broader society amidst a changing climate.&#x0D; Unique Contribution to Theory, Practice and Policy: The Prospect theory, Agency theory and the Resilience theory may be used to anchor future studies relating to climate change risk on insurance portfolios. Insurers are advised to invest in advanced risk modeling, diversify risk exposure across regions and lines of business, integrate climate risk into underwriting and pricing, strengthen regulatory oversight, collaborate with stakeholders, and invest in climate adaptation and mitigation measures. These recommendations aim to improve insurers' ability to quantify and manage climate-related risks effectively, promote sustainability, and contribute to broader resilience-building efforts in the face of climate change.&#x0D; Keywords: Climate Change Risk, Insurance Portfolios, Resilience, Risk Modeling, Diversification, Underwriting, Regulatory Oversight, Collaboration

Genetics of gambling disorder and related phenotypes: The potential uses of polygenic and multifactorial risk models to enable early detection and improve clinical outcomes.

Gambling Disorder (GD) is an impactful behavioural addiction for which there appear to be underpinning genetic contributors. Twin studies show significant GD heritability results and intergenerational transmission show high rates of transmission. Recent developments in polygenic and multifactorial risk prediction modelling provide promising opportunities to enable early identification and intervention for at risk individuals. People with GD often have significant delays in diagnosis and subsequent help-seeking that can compromise their recovery. In this paper we advocate for more research into the utility of polygenic and multifactorial risk modelling in GD research and treatment programs and rigorous evaluation of its costs and benefits.